Modular, distributed and multi-core architectures are currently considered a
promising approach for scalability of quantum computing systems. The
integration of multiple Quantum Processing Units necessitates classical and
quantum-coherent communication, introducing challenges related to noise and
quantum decoherence in quantum state transfers between cores. Optimizing
communication becomes imperative, and the compilation and mapping of quantum
circuits onto physical qubits must minimize state transfers while adhering to
architectural constraints. The compilation process, inherently an NP-hard
problem, demands extensive search times even with a small number of qubits to
be solved to optimality. To address this challenge efficiently, we advocate for
the utilization of heuristic mappers that can rapidly generate solutions. In
this work, we propose a novel approach employing Deep Reinforcement Learning
(DRL) methods to learn these heuristics for a specific multi-core architecture.
Our DRL agent incorporates a Transformer encoder and Graph Neural Networks. It
encodes quantum circuits using self-attention mechanisms and produce outputs
through an attention-based pointer mechanism that directly signifies the
probability of matching logical qubits with physical cores. This enables the
selection of optimal cores for logical qubits efficiently. Experimental
evaluations show that the proposed method can outperform baseline approaches in
terms of reducing inter-core communications and minimizing online
time-to-solution. This research contributes to the advancement of scalable
quantum computing systems by introducing a novel learning-based heuristic
approach for efficient quantum circuit compilation and mapping.

利用深度强化学习的方法，以及模块化分布式体系结构，提出了一种高效的量子电路编译和映射的新型学习启发式方法，以解决量子计算系统中的通信最优化问题。

模块量子架构中基于注意力机制的深度强化学习用于量子比特分配

Attention-Based Deep Reinforcement Learning for Qubit Allocation in  Modular Quantum Architectures

Compiling a quantum circuit for specific quantum hardware is a challenging
task. Moreover, current quantum computers have severe hardware limitations. To
make the most use of the limited resources, the compilation process should be
optimized. To improve currents methods, Reinforcement Learning (RL), a
technique in which an agent interacts with an environment to learn complex
policies to attain a specific goal, can be used. In this work, we present qgym,
a software framework derived from the OpenAI gym, together with environments
that are specifically tailored towards quantum compilation. The goal of qgym is
to connect the research fields of Artificial Intelligence (AI) with quantum
compilation by abstracting parts of the process that are irrelevant to either
domain. It can be used to train and benchmark RL agents and algorithms in
highly customizable environments.

编写特定量子硬件的量子电路是具有挑战性的任务，并且当前的量子计算机存在严重的硬件限制。为了充分利用有限的资源，应该优化编译过程。为了改进当前的方法，可以使用强化学习（RL），这是一种让代理与环境交互以学习复杂策略以达到特定目标的技术。在这项工作中，我们介绍了 qgym，这是一个从 OpenAI Gym 派生的软件框架，以及专门针对量子编译的环境。qgym 的目标是通过抽象与两个领域都无关的过程部分，将人工智能（AI）的研究领域与量子编译连接起来。它可用于在高度可定制的环境中训练和基准测试 RL 代理和算法。